The Effect of Personality on Occupational Stress in Veterinary Surgeons
Bibliographic record
Abstract
Statistics show that veterinary surgeons are in one of the professions with the highest suicide rates. This indicates the sector has significant well-being issues, with high levels of occupational stress and burnout. Previous research has focused on environmental factors in isolation, overlooking the influence of personality. This study aimed to establish that personality is a better predictor of occupational stress than environment. UK veterinary surgeons (n=311) completed an online survey composed of three questionnaires; the NEO Five-Factor Inventory, the Maslach Burnout Inventory, and the Job Stress Survey. Multiple regression analysis revealed that personality is a better predictor of occupational stress than environment (p<.001). Neuroticism is the trait that significantly predicts occupational stress (p<.001), and the components of neuroticism that contribute the most to stress are depression (p=.002) and anger hostility (p=.005). Demographic factors such as the number of years the veterinarian has been qualified acted as a mediator between depression and occupational stress (p<.001), and as a moderator between personal accomplishments and occupational stress (p=.028). Overall findings suggest that newly qualified veterinarians are at greater risk of suffering from high levels of occupational stress than those well established in the profession, and that veterinarians with higher levels of depression and anger hostility are likely to experience greater levels of occupational stress. Implications highlight the need for greater awareness of potentially susceptible personality traits in the veterinary admissions process. This would allow for the identification of those at risk and the implementation of interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".